Machine Learning Abel Sanchez John Williams Machine Learning
Machine Learning Abel Sanchez, John Williams
Machine Learning (Practical Definition) • Start with data • Learn a model from data, identify a pattern in data • Use pattern to gain insight
Examples • Prediction – e. g. Is this transaction fraudulent? • Prediction – e. g. Will user click on link? • Exploration – e. g. Processing larger numbers of documents • Exploration – e. g. Visualization patterns
Your Life is Impacted by ML • • • What adds you are shown What news articles you are offered Other shoppers like you bought … E-dating Security screening. . .
Linear Regression
Least Squares d 4 D = (d 1)2 + (d 2)2 + (d 3)2 + (d 4)2 Best linear model will have the smallest sum of distances squared (D) d 3 d 1 d 2
Linear Regression Slope Intercept Regression Equation Line
Active Learning • Write a function to create the linear regression equation …
K-Means
Algorithm 1. Place K points into the space represented by the objects. These points represent initial group centroids. 2. Assign each object to the group that has the closest centroid. 3. When all objects have been assigned, recalculate the positions of the K centroids. 4. Repeat Steps 2 and 3 until the centroids no longer move.
K-Means Algorithm start add k points (initial centroids) Assign each point to closest centroid Group based on assigned centroid Recalculate k centroids yes no Centroid movement end
Visualization
progress values 0 – to – 1 Start x 1 , y 1 xi = x 1 + (x 2 – x 1) progress yi = y 1 + (y 2 – y 1) progress xi, yi y 2 – y 1 x 2 , y 2 End progress x 2 – x 1
You have x 1, y 1 and x 2, y 2 - Find yi given xi Slope, m = (y 2 – y 1)/(x 2 – x 1) Start Substitute xi, yi for x 2, y 2 x 1 , y 1 The equation is: y = y 1 + m(x – x 1) y 2 – y 1 yi x 2 , y 2 xi progress x 2 – x 1 End
Active Learning • Write an implementation of K-Means …
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